Evaluation of the Performance of Evolutionary Algorithms for Optimization of Low-Enthalpy Geothermal Heating Plants

In this paper, we present the application of Evolutionary Algorithms (EAs) and linear programming for minimizing thermal impacts in the ground by operating a low-enthalpy geothermal plant with a field of multiple borehole heat exchangers (BHEs). The new methodology is demonstrated on two synthetic case studies with 36 BHEs that are grounded in reality and operated to produce given seasonal heating energy demand. We compare the performance of six different Evolutionary Algorithms (EAs) (two Differential Evolution variants, Particle Swarm Optimization, two Evolution Strategy based Algorithms, real valued Genetic Algorithm) and Monte-Carlo random search to find the optimal BHE positions. Additionally, linear programming is applied to adjust the energy extraction (loads) for the individual BHEs in the field. Both optimization steps are applied separately and in combination, and the achieved system improvements are compared to the conditions for the non-optimized case. The EAs were able to find constellations that cause less pronounced temperature changes in the subsurface (18%-25%) than those associated with non-optimized BHE fields. Further, we could show that exclusive optimization of BHE energy extraction rates delivers slightly better results than the optimization of BHE positions. Combining both optimization approaches is the best choice and, ideally, adjusts the geothermal plant.